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Objects are Python’s abstraction for data. All data in a Python program
is represented by objects or by relations between objects. (In a sense, and in
conformance to Von Neumann’s model of a “stored program computer,” code is also
represented by objects.)

Every object has an identity, a type and a value. An object’s identity never
changes once it has been created; you may think of it as the object’s address in
memory. The ‘is‘ operator compares the identity of two objects; the
id() function returns an integer representing its identity (currently
implemented as its address). An object’s type is also unchangeable. [1]
An object’s type determines the operations that the object supports (e.g., “does
it have a length?”) and also defines the possible values for objects of that
type. The type() function returns an object’s type (which is an object
itself). The value of some objects can change. Objects whose value can
change are said to be mutable; objects whose value is unchangeable once they
are created are called immutable. (The value of an immutable container object
that contains a reference to a mutable object can change when the latter’s value
is changed; however the container is still considered immutable, because the
collection of objects it contains cannot be changed. So, immutability is not
strictly the same as having an unchangeable value, it is more subtle.) An
object’s mutability is determined by its type; for instance, numbers, strings
and tuples are immutable, while dictionaries and lists are mutable.

Objects are never explicitly destroyed; however, when they become unreachable
they may be garbage-collected. An implementation is allowed to postpone garbage
collection or omit it altogether — it is a matter of implementation quality
how garbage collection is implemented, as long as no objects are collected that
are still reachable.

CPython implementation detail: CPython currently uses a reference-counting scheme with (optional) delayed
detection of cyclically linked garbage, which collects most objects as soon
as they become unreachable, but is not guaranteed to collect garbage
containing circular references. See the documentation of the gc
module for information on controlling the collection of cyclic garbage.
Other implementations act differently and CPython may change.
Do not depend on immediate finalization of objects when they become
unreachable (ex: always close files).

Note that the use of the implementation’s tracing or debugging facilities may
keep objects alive that would normally be collectable. Also note that catching
an exception with a ‘try...except‘ statement may keep
objects alive.

Some objects contain references to “external” resources such as open files or
windows. It is understood that these resources are freed when the object is
garbage-collected, but since garbage collection is not guaranteed to happen,
such objects also provide an explicit way to release the external resource,
usually a close() method. Programs are strongly recommended to explicitly
close such objects. The ‘try...finally‘ statement
provides a convenient way to do this.

Some objects contain references to other objects; these are called containers.
Examples of containers are tuples, lists and dictionaries. The references are
part of a container’s value. In most cases, when we talk about the value of a
container, we imply the values, not the identities of the contained objects;
however, when we talk about the mutability of a container, only the identities
of the immediately contained objects are implied. So, if an immutable container
(like a tuple) contains a reference to a mutable object, its value changes if
that mutable object is changed.

Types affect almost all aspects of object behavior. Even the importance of
object identity is affected in some sense: for immutable types, operations that
compute new values may actually return a reference to any existing object with
the same type and value, while for mutable objects this is not allowed. E.g.,
after a=1;b=1, a and b may or may not refer to the same object
with the value one, depending on the implementation, but after c=[];d=[], c and d are guaranteed to refer to two different, unique, newly
created empty lists. (Note that c=d=[] assigns the same object to both
c and d.)

Below is a list of the types that are built into Python. Extension modules
(written in C, Java, or other languages, depending on the implementation) can
define additional types. Future versions of Python may add types to the type
hierarchy (e.g., rational numbers, efficiently stored arrays of integers, etc.).

Some of the type descriptions below contain a paragraph listing ‘special
attributes.’ These are attributes that provide access to the implementation and
are not intended for general use. Their definition may change in the future.

None

This type has a single value. There is a single object with this value. This
object is accessed through the built-in name None. It is used to signify the
absence of a value in many situations, e.g., it is returned from functions that
don’t explicitly return anything. Its truth value is false.

NotImplemented

This type has a single value. There is a single object with this value. This
object is accessed through the built-in name NotImplemented. Numeric methods
and rich comparison methods may return this value if they do not implement the
operation for the operands provided. (The interpreter will then try the
reflected operation, or some other fallback, depending on the operator.) Its
truth value is true.

Ellipsis

This type has a single value. There is a single object with this value. This
object is accessed through the built-in name Ellipsis. It is used to
indicate the presence of the ... syntax in a slice. Its truth value is
true.

These are created by numeric literals and returned as results by arithmetic
operators and arithmetic built-in functions. Numeric objects are immutable;
once created their value never changes. Python numbers are of course strongly
related to mathematical numbers, but subject to the limitations of numerical
representation in computers.

Python distinguishes between integers, floating point numbers, and complex
numbers:

These represent elements from the mathematical set of integers (positive and
negative).

There are three types of integers:

Plain integers

These represent numbers in the range -2147483648 through 2147483647.
(The range may be larger on machines with a larger natural word size,
but not smaller.) When the result of an operation would fall outside
this range, the result is normally returned as a long integer (in some
cases, the exception OverflowError is raised instead). For the
purpose of shift and mask operations, integers are assumed to have a
binary, 2’s complement notation using 32 or more bits, and hiding no
bits from the user (i.e., all 4294967296 different bit patterns
correspond to different values).

Long integers

These represent numbers in an unlimited range, subject to available
(virtual) memory only. For the purpose of shift and mask operations, a
binary representation is assumed, and negative numbers are represented
in a variant of 2’s complement which gives the illusion of an infinite
string of sign bits extending to the left.

Booleans

These represent the truth values False and True. The two objects
representing the values False and True are the only Boolean objects.
The Boolean type is a subtype of plain integers, and Boolean values
behave like the values 0 and 1, respectively, in almost all contexts,
the exception being that when converted to a string, the strings
"False" or "True" are returned, respectively.

The rules for integer representation are intended to give the most
meaningful interpretation of shift and mask operations involving negative
integers and the least surprises when switching between the plain and long
integer domains. Any operation, if it yields a result in the plain
integer domain, will yield the same result in the long integer domain or
when using mixed operands. The switch between domains is transparent to
the programmer.

These represent machine-level double precision floating point numbers. You are
at the mercy of the underlying machine architecture (and C or Java
implementation) for the accepted range and handling of overflow. Python does not
support single-precision floating point numbers; the savings in processor and
memory usage that are usually the reason for using these are dwarfed by the
overhead of using objects in Python, so there is no reason to complicate the
language with two kinds of floating point numbers.

These represent complex numbers as a pair of machine-level double precision
floating point numbers. The same caveats apply as for floating point numbers.
The real and imaginary parts of a complex number z can be retrieved through
the read-only attributes z.real and z.imag.

Sequences

These represent finite ordered sets indexed by non-negative numbers. The
built-in function len() returns the number of items of a sequence. When
the length of a sequence is n, the index set contains the numbers 0, 1,
..., n-1. Item i of sequence a is selected by a[i].

Sequences also support slicing: a[i:j] selects all items with index k such
that i<=k<j. When used as an expression, a slice is a
sequence of the same type. This implies that the index set is renumbered so
that it starts at 0.

Some sequences also support “extended slicing” with a third “step” parameter:
a[i:j:k] selects all items of a with index x where x=i+n*k, n>=0 and i<=x<j.

Sequences are distinguished according to their mutability:

Immutable sequences

An object of an immutable sequence type cannot change once it is created. (If
the object contains references to other objects, these other objects may be
mutable and may be changed; however, the collection of objects directly
referenced by an immutable object cannot change.)

The following types are immutable sequences:

Strings

The items of a string are characters. There is no separate character type; a
character is represented by a string of one item. Characters represent (at
least) 8-bit bytes. The built-in functions chr() and ord() convert
between characters and nonnegative integers representing the byte values. Bytes
with the values 0-127 usually represent the corresponding ASCII values, but the
interpretation of values is up to the program. The string data type is also
used to represent arrays of bytes, e.g., to hold data read from a file.

(On systems whose native character set is not ASCII, strings may use EBCDIC in
their internal representation, provided the functions chr() and
ord() implement a mapping between ASCII and EBCDIC, and string comparison
preserves the ASCII order. Or perhaps someone can propose a better rule?)

Unicode

The items of a Unicode object are Unicode code units. A Unicode code unit is
represented by a Unicode object of one item and can hold either a 16-bit or
32-bit value representing a Unicode ordinal (the maximum value for the ordinal
is given in sys.maxunicode, and depends on how Python is configured at
compile time). Surrogate pairs may be present in the Unicode object, and will
be reported as two separate items. The built-in functions unichr() and
ord() convert between code units and nonnegative integers representing the
Unicode ordinals as defined in the Unicode Standard 3.0. Conversion from and to
other encodings are possible through the Unicode method encode() and the
built-in function unicode().

Tuples

The items of a tuple are arbitrary Python objects. Tuples of two or more items
are formed by comma-separated lists of expressions. A tuple of one item (a
‘singleton’) can be formed by affixing a comma to an expression (an expression
by itself does not create a tuple, since parentheses must be usable for grouping
of expressions). An empty tuple can be formed by an empty pair of parentheses.

Mutable sequences

Mutable sequences can be changed after they are created. The subscription and
slicing notations can be used as the target of assignment and del
(delete) statements.

There are currently two intrinsic mutable sequence types:

Lists

The items of a list are arbitrary Python objects. Lists are formed by placing a
comma-separated list of expressions in square brackets. (Note that there are no
special cases needed to form lists of length 0 or 1.)

Byte Arrays

A bytearray object is a mutable array. They are created by the built-in
bytearray() constructor. Aside from being mutable (and hence
unhashable), byte arrays otherwise provide the same interface and
functionality as immutable bytes objects.

The extension module array provides an additional example of a mutable
sequence type.

Set types

These represent unordered, finite sets of unique, immutable objects. As such,
they cannot be indexed by any subscript. However, they can be iterated over, and
the built-in function len() returns the number of items in a set. Common
uses for sets are fast membership testing, removing duplicates from a sequence,
and computing mathematical operations such as intersection, union, difference,
and symmetric difference.

For set elements, the same immutability rules apply as for dictionary keys. Note
that numeric types obey the normal rules for numeric comparison: if two numbers
compare equal (e.g., 1 and 1.0), only one of them can be contained in a
set.

There are currently two intrinsic set types:

Sets

These represent a mutable set. They are created by the built-in set()
constructor and can be modified afterwards by several methods, such as
add().

Frozen sets

These represent an immutable set. They are created by the built-in
frozenset() constructor. As a frozenset is immutable and
hashable, it can be used again as an element of another set, or as
a dictionary key.

Mappings

These represent finite sets of objects indexed by arbitrary index sets. The
subscript notation a[k] selects the item indexed by k from the mapping
a; this can be used in expressions and as the target of assignments or
del statements. The built-in function len() returns the number
of items in a mapping.

There is currently a single intrinsic mapping type:

Dictionaries

These represent finite sets of objects indexed by nearly arbitrary values. The
only types of values not acceptable as keys are values containing lists or
dictionaries or other mutable types that are compared by value rather than by
object identity, the reason being that the efficient implementation of
dictionaries requires a key’s hash value to remain constant. Numeric types used
for keys obey the normal rules for numeric comparison: if two numbers compare
equal (e.g., 1 and 1.0) then they can be used interchangeably to index
the same dictionary entry.

Dictionaries are mutable; they can be created by the {...} notation (see
section Dictionary displays).

These are the types to which the function call operation (see section
Calls) can be applied:

User-defined functions

A user-defined function object is created by a function definition (see
section Function definitions). It should be called with an argument list
containing the same number of items as the function’s formal parameter
list.

Special attributes:

Attribute

Meaning

__doc__func_doc

The function’s documentation
string, or None if
unavailable.

Writable

__name__func_name

The function’s name.

Writable

__module__

The name of the module the
function was defined in, or
None if unavailable.

Writable

__defaults__func_defaults

A tuple containing default
argument values for those
arguments that have defaults,
or None if no arguments
have a default value.

Writable

__code__func_code

The code object representing
the compiled function body.

Writable

__globals__func_globals

A reference to the dictionary
that holds the function’s
global variables — the
global namespace of the
module in which the function
was defined.

Read-only

__dict__func_dict

The namespace supporting
arbitrary function
attributes.

Writable

__closure__func_closure

None or a tuple of cells
that contain bindings for the
function’s free variables.

Read-only

Most of the attributes labelled “Writable” check the type of the assigned value.

Changed in version 2.4: func_name is now writable.

Changed in version 2.6: The double-underscore attributes __closure__, __code__,
__defaults__, and __globals__ were introduced as aliases for
the corresponding func_* attributes for forwards compatibility
with Python 3.

Function objects also support getting and setting arbitrary attributes, which
can be used, for example, to attach metadata to functions. Regular attribute
dot-notation is used to get and set such attributes. Note that the current
implementation only supports function attributes on user-defined functions.
Function attributes on built-in functions may be supported in the future.

Additional information about a function’s definition can be retrieved from its
code object; see the description of internal types below.

User-defined methods

A user-defined method object combines a class, a class instance (or None)
and any callable object (normally a user-defined function).

Special read-only attributes: im_self is the class instance object,
im_func is the function object; im_class is the class of
im_self for bound methods or the class that asked for the method for
unbound methods; __doc__ is the method’s documentation (same as
im_func.__doc__); __name__ is the method name (same as
im_func.__name__); __module__ is the name of the module the method
was defined in, or None if unavailable.

Changed in version 2.2: im_self used to refer to the class that defined the method.

Changed in version 2.6: For Python 3 forward-compatibility, im_func is also available as
__func__, and im_self as __self__.

Methods also support accessing (but not setting) the arbitrary function
attributes on the underlying function object.

User-defined method objects may be created when getting an attribute of a class
(perhaps via an instance of that class), if that attribute is a user-defined
function object, an unbound user-defined method object, or a class method
object. When the attribute is a user-defined method object, a new method object
is only created if the class from which it is being retrieved is the same as, or
a derived class of, the class stored in the original method object; otherwise,
the original method object is used as it is.

When a user-defined method object is created by retrieving a user-defined
function object from a class, its im_self attribute is None
and the method object is said to be unbound. When one is created by
retrieving a user-defined function object from a class via one of its
instances, its im_self attribute is the instance, and the method
object is said to be bound. In either case, the new method’s
im_class attribute is the class from which the retrieval takes
place, and its im_func attribute is the original function object.

When a user-defined method object is created by retrieving another method object
from a class or instance, the behaviour is the same as for a function object,
except that the im_func attribute of the new instance is not the
original method object but its im_func attribute.

When a user-defined method object is created by retrieving a class method object
from a class or instance, its im_self attribute is the class itself, and
its im_func attribute is the function object underlying the class method.

When an unbound user-defined method object is called, the underlying function
(im_func) is called, with the restriction that the first argument must
be an instance of the proper class (im_class) or of a derived class
thereof.

When a bound user-defined method object is called, the underlying function
(im_func) is called, inserting the class instance (im_self) in
front of the argument list. For instance, when C is a class which
contains a definition for a function f(), and x is an instance of
C, calling x.f(1) is equivalent to calling C.f(x,1).

When a user-defined method object is derived from a class method object, the
“class instance” stored in im_self will actually be the class itself, so
that calling either x.f(1) or C.f(1) is equivalent to calling f(C,1)
where f is the underlying function.

Note that the transformation from function object to (unbound or bound) method
object happens each time the attribute is retrieved from the class or instance.
In some cases, a fruitful optimization is to assign the attribute to a local
variable and call that local variable. Also notice that this transformation only
happens for user-defined functions; other callable objects (and all non-callable
objects) are retrieved without transformation. It is also important to note
that user-defined functions which are attributes of a class instance are not
converted to bound methods; this only happens when the function is an
attribute of the class.

Generator functions

A function or method which uses the yield statement (see section
The yield statement) is called a generator
function. Such a function, when called, always returns an iterator object
which can be used to execute the body of the function: calling the iterator’s
next() method will cause the function to execute until
it provides a value
using the yield statement. When the function executes a
return statement or falls off the end, a StopIteration
exception is raised and the iterator will have reached the end of the set of
values to be returned.

Built-in functions

A built-in function object is a wrapper around a C function. Examples of
built-in functions are len() and math.sin() (math is a
standard built-in module). The number and type of the arguments are
determined by the C function. Special read-only attributes:
__doc__ is the function’s documentation string, or None if
unavailable; __name__ is the function’s name; __self__ is
set to None (but see the next item); __module__ is the name of
the module the function was defined in or None if unavailable.

Built-in methods

This is really a different disguise of a built-in function, this time containing
an object passed to the C function as an implicit extra argument. An example of
a built-in method is alist.append(), assuming alist is a list object. In
this case, the special read-only attribute __self__ is set to the object
denoted by alist.

Class Types

Class types, or “new-style classes,” are callable. These objects normally act
as factories for new instances of themselves, but variations are possible for
class types that override __new__(). The arguments of the call are passed
to __new__() and, in the typical case, to __init__() to initialize
the new instance.

Classic Classes

Class objects are described below. When a class object is called, a new class
instance (also described below) is created and returned. This implies a call to
the class’s __init__() method if it has one. Any arguments are passed on
to the __init__() method. If there is no __init__() method, the
class must be called without arguments.

Class instances

Class instances are described below. Class instances are callable only when the
class has a __call__() method; x(arguments) is a shorthand for
x.__call__(arguments).

Modules

Modules are imported by the import statement (see section
The import statement). A module object has a
namespace implemented by a dictionary object (this is the dictionary referenced
by the func_globals attribute of functions defined in the module). Attribute
references are translated to lookups in this dictionary, e.g., m.x is
equivalent to m.__dict__["x"]. A module object does not contain the code
object used to initialize the module (since it isn’t needed once the
initialization is done).

Special read-only attribute: __dict__ is the module’s namespace as a
dictionary object.

CPython implementation detail: Because of the way CPython clears module dictionaries, the module
dictionary will be cleared when the module falls out of scope even if the
dictionary still has live references. To avoid this, copy the dictionary
or keep the module around while using its dictionary directly.

Predefined (writable) attributes: __name__ is the module’s name;
__doc__ is the module’s documentation string, or None if
unavailable; __file__ is the pathname of the file from which the module
was loaded, if it was loaded from a file. The __file__ attribute is not
present for C modules that are statically linked into the interpreter; for
extension modules loaded dynamically from a shared library, it is the pathname
of the shared library file.

Classes

Both class types (new-style classes) and class objects (old-style/classic
classes) are typically created by class definitions (see section
Class definitions). A class has a namespace implemented by a dictionary object.
Class attribute references are translated to lookups in this dictionary, e.g.,
C.x is translated to C.__dict__["x"] (although for new-style classes
in particular there are a number of hooks which allow for other means of
locating attributes). When the attribute name is not found there, the
attribute search continues in the base classes. For old-style classes, the
search is depth-first, left-to-right in the order of occurrence in the base
class list. New-style classes use the more complex C3 method resolution
order which behaves correctly even in the presence of ‘diamond’
inheritance structures where there are multiple inheritance paths
leading back to a common ancestor. Additional details on the C3 MRO used by
new-style classes can be found in the documentation accompanying the
2.3 release at https://www.python.org/download/releases/2.3/mro/.

When a class attribute reference (for class C, say) would yield a
user-defined function object or an unbound user-defined method object whose
associated class is either C or one of its base classes, it is
transformed into an unbound user-defined method object whose im_class
attribute is C. When it would yield a class method object, it is
transformed into a bound user-defined method object whose
im_self attribute is C. When it would yield a
static method object, it is transformed into the object wrapped by the static
method object. See section Implementing Descriptors for another way in which
attributes retrieved from a class may differ from those actually contained in
its __dict__ (note that only new-style classes support descriptors).

Class attribute assignments update the class’s dictionary, never the dictionary
of a base class.

A class object can be called (see above) to yield a class instance (see below).

Special attributes: __name__ is the class name; __module__ is
the module name in which the class was defined; __dict__ is the
dictionary containing the class’s namespace; __bases__ is a
tuple (possibly empty or a singleton) containing the base classes, in the
order of their occurrence in the base class list; __doc__ is the
class’s documentation string, or None if undefined.

Class instances

A class instance is created by calling a class object (see above). A class
instance has a namespace implemented as a dictionary which is the first place in
which attribute references are searched. When an attribute is not found there,
and the instance’s class has an attribute by that name, the search continues
with the class attributes. If a class attribute is found that is a user-defined
function object or an unbound user-defined method object whose associated class
is the class (call it C) of the instance for which the attribute
reference was initiated or one of its bases, it is transformed into a bound
user-defined method object whose im_class attribute is C and
whose im_self attribute is the instance. Static method and class method
objects are also transformed, as if they had been retrieved from class
C; see above under “Classes”. See section Implementing Descriptors for
another way in which attributes of a class retrieved via its instances may
differ from the objects actually stored in the class’s __dict__. If no
class attribute is found, and the object’s class has a __getattr__()
method, that is called to satisfy the lookup.

Attribute assignments and deletions update the instance’s dictionary, never a
class’s dictionary. If the class has a __setattr__() or
__delattr__() method, this is called instead of updating the instance
dictionary directly.

Class instances can pretend to be numbers, sequences, or mappings if they have
methods with certain special names. See section Special method names.

Special attributes: __dict__ is the attribute dictionary;
__class__ is the instance’s class.

Files

A file object represents an open file. File objects are created by the
open() built-in function, and also by os.popen(),
os.fdopen(), and the makefile() method of socket objects (and
perhaps by other functions or methods provided by extension modules). The
objects sys.stdin, sys.stdout and sys.stderr are initialized to
file objects corresponding to the interpreter’s standard input, output and
error streams. See File Objects for complete documentation of
file objects.

Internal types

A few types used internally by the interpreter are exposed to the user. Their
definitions may change with future versions of the interpreter, but they are
mentioned here for completeness.

Code objects

Code objects represent byte-compiled executable Python code, or bytecode.
The difference between a code object and a function object is that the function
object contains an explicit reference to the function’s globals (the module in
which it was defined), while a code object contains no context; also the default
argument values are stored in the function object, not in the code object
(because they represent values calculated at run-time). Unlike function
objects, code objects are immutable and contain no references (directly or
indirectly) to mutable objects.

Special read-only attributes: co_name gives the function name;
co_argcount is the number of positional arguments (including arguments
with default values); co_nlocals is the number of local variables used
by the function (including arguments); co_varnames is a tuple containing
the names of the local variables (starting with the argument names);
co_cellvars is a tuple containing the names of local variables that are
referenced by nested functions; co_freevars is a tuple containing the
names of free variables; co_code is a string representing the sequence
of bytecode instructions; co_consts is a tuple containing the literals
used by the bytecode; co_names is a tuple containing the names used by
the bytecode; co_filename is the filename from which the code was
compiled; co_firstlineno is the first line number of the function;
co_lnotab is a string encoding the mapping from bytecode offsets to
line numbers (for details see the source code of the interpreter);
co_stacksize is the required stack size (including local variables);
co_flags is an integer encoding a number of flags for the interpreter.

The following flag bits are defined for co_flags: bit 0x04 is set if
the function uses the *arguments syntax to accept an arbitrary number of
positional arguments; bit 0x08 is set if the function uses the
**keywords syntax to accept arbitrary keyword arguments; bit 0x20 is set
if the function is a generator.

Future feature declarations (from__future__importdivision) also use bits
in co_flags to indicate whether a code object was compiled with a
particular feature enabled: bit 0x2000 is set if the function was compiled
with future division enabled; bits 0x10 and 0x1000 were used in earlier
versions of Python.

Other bits in co_flags are reserved for internal use.

If a code object represents a function, the first item in co_consts is
the documentation string of the function, or None if undefined.

Special read-only attributes: f_back is to the previous stack frame
(towards the caller), or None if this is the bottom stack frame;
f_code is the code object being executed in this frame; f_locals
is the dictionary used to look up local variables; f_globals is used for
global variables; f_builtins is used for built-in (intrinsic) names;
f_restricted is a flag indicating whether the function is executing in
restricted execution mode; f_lasti gives the precise instruction (this
is an index into the bytecode string of the code object).

Special writable attributes: f_trace, if not None, is a function
called at the start of each source code line (this is used by the debugger);
f_exc_type, f_exc_value, f_exc_traceback represent the
last exception raised in the parent frame provided another exception was ever
raised in the current frame (in all other cases they are None); f_lineno
is the current line number of the frame — writing to this from within a trace
function jumps to the given line (only for the bottom-most frame). A debugger
can implement a Jump command (aka Set Next Statement) by writing to f_lineno.

Traceback objects

Traceback objects represent a stack trace of an exception. A traceback object
is created when an exception occurs. When the search for an exception handler
unwinds the execution stack, at each unwound level a traceback object is
inserted in front of the current traceback. When an exception handler is
entered, the stack trace is made available to the program. (See section
The try statement.) It is accessible as sys.exc_traceback,
and also as the third item of the tuple returned by sys.exc_info(). The
latter is the preferred interface, since it works correctly when the program is
using multiple threads. When the program contains no suitable handler, the stack
trace is written (nicely formatted) to the standard error stream; if the
interpreter is interactive, it is also made available to the user as
sys.last_traceback.

Special read-only attributes: tb_next is the next level in the stack
trace (towards the frame where the exception occurred), or None if there is
no next level; tb_frame points to the execution frame of the current
level; tb_lineno gives the line number where the exception occurred;
tb_lasti indicates the precise instruction. The line number and last
instruction in the traceback may differ from the line number of its frame object
if the exception occurred in a try statement with no matching except
clause or with a finally clause.

Slice objects

Slice objects are used to represent slices when extended slice syntax is used.
This is a slice using two colons, or multiple slices or ellipses separated by
commas, e.g., a[i:j:step], a[i:j,k:l], or a[...,i:j]. They are
also created by the built-in slice() function.

Special read-only attributes: start is the lower bound;
stop is the upper bound; step is the step
value; each is None if omitted. These attributes can have any type.

This method takes a single integer argument length and computes information
about the extended slice that the slice object would describe if applied to a
sequence of length items. It returns a tuple of three integers; respectively
these are the start and stop indices and the step or stride length of the
slice. Missing or out-of-bounds indices are handled in a manner consistent with
regular slices.

New in version 2.3.

Static method objects

Static method objects provide a way of defeating the transformation of function
objects to method objects described above. A static method object is a wrapper
around any other object, usually a user-defined method object. When a static
method object is retrieved from a class or a class instance, the object actually
returned is the wrapped object, which is not subject to any further
transformation. Static method objects are not themselves callable, although the
objects they wrap usually are. Static method objects are created by the built-in
staticmethod() constructor.

Class method objects

A class method object, like a static method object, is a wrapper around another
object that alters the way in which that object is retrieved from classes and
class instances. The behaviour of class method objects upon such retrieval is
described above, under “User-defined methods”. Class method objects are created
by the built-in classmethod() constructor.

Classes and instances come in two flavors: old-style (or classic) and new-style.

Up to Python 2.1 the concept of class was unrelated to the concept of
type, and old-style classes were the only flavor available. For an
old-style class, the statement x.__class__ provides the class of x, but
type(x) is always <type'instance'>. This reflects the fact that all
old-style instances, independent of their class, are implemented with a single
built-in type, called instance.

New-style classes were introduced in Python 2.2 to unify the concepts of
class and type. A new-style class is simply a user-defined type,
no more, no less. If x is an instance of a new-style class, then type(x)
is typically the same as x.__class__ (although this is not guaranteed – a
new-style class instance is permitted to override the value returned for
x.__class__).

The major motivation for introducing new-style classes is to provide a unified
object model with a full meta-model. It also has a number of practical
benefits, like the ability to subclass most built-in types, or the introduction
of “descriptors”, which enable computed properties.

For compatibility reasons, classes are still old-style by default. New-style
classes are created by specifying another new-style class (i.e. a type) as a
parent class, or the “top-level type” object if no other parent is
needed. The behaviour of new-style classes differs from that of old-style
classes in a number of important details in addition to what type()
returns. Some of these changes are fundamental to the new object model, like
the way special methods are invoked. Others are “fixes” that could not be
implemented before for compatibility concerns, like the method resolution order
in case of multiple inheritance.

While this manual aims to provide comprehensive coverage of Python’s class
mechanics, it may still be lacking in some areas when it comes to its coverage
of new-style classes. Please see https://www.python.org/doc/newstyle/ for
sources of additional information.

A class can implement certain operations that are invoked by special syntax
(such as arithmetic operations or subscripting and slicing) by defining methods
with special names. This is Python’s approach to operator overloading,
allowing classes to define their own behavior with respect to language
operators. For instance, if a class defines a method named __getitem__(),
and x is an instance of this class, then x[i] is roughly equivalent
to x.__getitem__(i) for old-style classes and type(x).__getitem__(x,i)
for new-style classes. Except where mentioned, attempts to execute an
operation raise an exception when no appropriate method is defined (typically
AttributeError or TypeError).

When implementing a class that emulates any built-in type, it is important that
the emulation only be implemented to the degree that it makes sense for the
object being modelled. For example, some sequences may work well with retrieval
of individual elements, but extracting a slice may not make sense. (One example
of this is the NodeList interface in the W3C’s Document
Object Model.)

Called to create a new instance of class cls. __new__() is a static
method (special-cased so you need not declare it as such) that takes the class
of which an instance was requested as its first argument. The remaining
arguments are those passed to the object constructor expression (the call to the
class). The return value of __new__() should be the new object instance
(usually an instance of cls).

Typical implementations create a new instance of the class by invoking the
superclass’s __new__() method using super(currentclass,cls).__new__(cls[,...]) with appropriate arguments and then modifying the
newly-created instance as necessary before returning it.

If __new__() returns an instance of cls, then the new instance’s
__init__() method will be invoked like __init__(self[,...]), where
self is the new instance and the remaining arguments are the same as were
passed to __new__().

If __new__() does not return an instance of cls, then the new instance’s
__init__() method will not be invoked.

__new__() is intended mainly to allow subclasses of immutable types (like
int, str, or tuple) to customize instance creation. It is also commonly
overridden in custom metaclasses in order to customize class creation.

Called after the instance has been created (by __new__()), but before
it is returned to the caller. The arguments are those passed to the
class constructor expression. If a base class has an __init__() method,
the derived class’s __init__() method, if any, must explicitly call it to
ensure proper initialization of the base class part of the instance; for
example: BaseClass.__init__(self,[args...]).

Called when the instance is about to be destroyed. This is also called a
destructor. If a base class has a __del__() method, the derived class’s
__del__() method, if any, must explicitly call it to ensure proper
deletion of the base class part of the instance. Note that it is possible
(though not recommended!) for the __del__() method to postpone destruction
of the instance by creating a new reference to it. It may then be called at a
later time when this new reference is deleted. It is not guaranteed that
__del__() methods are called for objects that still exist when the
interpreter exits.

Note

delx doesn’t directly call x.__del__() — the former decrements
the reference count for x by one, and the latter is only called when
x‘s reference count reaches zero. Some common situations that may
prevent the reference count of an object from going to zero include:
circular references between objects (e.g., a doubly-linked list or a tree
data structure with parent and child pointers); a reference to the object
on the stack frame of a function that caught an exception (the traceback
stored in sys.exc_traceback keeps the stack frame alive); or a
reference to the object on the stack frame that raised an unhandled
exception in interactive mode (the traceback stored in
sys.last_traceback keeps the stack frame alive). The first situation
can only be remedied by explicitly breaking the cycles; the latter two
situations can be resolved by storing None in sys.exc_traceback or
sys.last_traceback. Circular references which are garbage are
detected when the option cycle detector is enabled (it’s on by default),
but can only be cleaned up if there are no Python-level __del__()
methods involved. Refer to the documentation for the gc module for
more information about how __del__() methods are handled by the
cycle detector, particularly the description of the garbage value.

Warning

Due to the precarious circumstances under which __del__() methods are
invoked, exceptions that occur during their execution are ignored, and a warning
is printed to sys.stderr instead. Also, when __del__() is invoked in
response to a module being deleted (e.g., when execution of the program is
done), other globals referenced by the __del__() method may already have
been deleted or in the process of being torn down (e.g. the import
machinery shutting down). For this reason, __del__() methods
should do the absolute
minimum needed to maintain external invariants. Starting with version 1.5,
Python guarantees that globals whose name begins with a single underscore are
deleted from their module before other globals are deleted; if no other
references to such globals exist, this may help in assuring that imported
modules are still available at the time when the __del__() method is
called.

Called by the repr() built-in function and by string conversions (reverse
quotes) to compute the “official” string representation of an object. If at all
possible, this should look like a valid Python expression that could be used to
recreate an object with the same value (given an appropriate environment). If
this is not possible, a string of the form <...someusefuldescription...>
should be returned. The return value must be a string object. If a class
defines __repr__() but not __str__(), then __repr__() is also
used when an “informal” string representation of instances of that class is
required.

This is typically used for debugging, so it is important that the representation
is information-rich and unambiguous.

Called by the str() built-in function and by the print
statement to compute the “informal” string representation of an object. This
differs from __repr__() in that it does not have to be a valid Python
expression: a more convenient or concise representation may be used instead.
The return value must be a string object.

These are the so-called “rich comparison” methods, and are called for comparison
operators in preference to __cmp__() below. The correspondence between
operator symbols and method names is as follows: x<y calls x.__lt__(y),
x<=y calls x.__le__(y), x==y calls x.__eq__(y), x!=y and
x<>y call x.__ne__(y), x>y calls x.__gt__(y), and x>=y calls
x.__ge__(y).

A rich comparison method may return the singleton NotImplemented if it does
not implement the operation for a given pair of arguments. By convention,
False and True are returned for a successful comparison. However, these
methods can return any value, so if the comparison operator is used in a Boolean
context (e.g., in the condition of an if statement), Python will call
bool() on the value to determine if the result is true or false.

There are no implied relationships among the comparison operators. The truth
of x==y does not imply that x!=y is false. Accordingly, when
defining __eq__(), one should also define __ne__() so that the
operators will behave as expected. See the paragraph on __hash__() for
some important notes on creating hashable objects which support
custom comparison operations and are usable as dictionary keys.

There are no swapped-argument versions of these methods (to be used when the
left argument does not support the operation but the right argument does);
rather, __lt__() and __gt__() are each other’s reflection,
__le__() and __ge__() are each other’s reflection, and
__eq__() and __ne__() are their own reflection.

Called by comparison operations if rich comparison (see above) is not
defined. Should return a negative integer if self<other, zero if
self==other, a positive integer if self>other. If no
__cmp__(), __eq__() or __ne__() operation is defined, class
instances are compared by object identity (“address”). See also the
description of __hash__() for some important notes on creating
hashable objects which support custom comparison operations and are
usable as dictionary keys. (Note: the restriction that exceptions are not
propagated by __cmp__() has been removed since Python 1.5.)

Called by built-in function hash() and for operations on members of
hashed collections including set, frozenset, and
dict. __hash__() should return an integer. The only required
property is that objects which compare equal have the same hash value; it is
advised to somehow mix together (e.g. using exclusive or) the hash values for
the components of the object that also play a part in comparison of objects.

If a class does not define a __cmp__() or __eq__() method it
should not define a __hash__() operation either; if it defines
__cmp__() or __eq__() but not __hash__(), its instances
will not be usable in hashed collections. If a class defines mutable objects
and implements a __cmp__() or __eq__() method, it should not
implement __hash__(), since hashable collection implementations require
that a object’s hash value is immutable (if the object’s hash value changes,
it will be in the wrong hash bucket).

User-defined classes have __cmp__() and __hash__() methods
by default; with them, all objects compare unequal (except with themselves)
and x.__hash__() returns a result derived from id(x).

Classes which inherit a __hash__() method from a parent class but
change the meaning of __cmp__() or __eq__() such that the hash
value returned is no longer appropriate (e.g. by switching to a value-based
concept of equality instead of the default identity based equality) can
explicitly flag themselves as being unhashable by setting __hash__=None
in the class definition. Doing so means that not only will instances of the
class raise an appropriate TypeError when a program attempts to
retrieve their hash value, but they will also be correctly identified as
unhashable when checking isinstance(obj,collections.Hashable) (unlike
classes which define their own __hash__() to explicitly raise
TypeError).

Changed in version 2.5: __hash__() may now also return a long integer object; the 32-bit
integer is then derived from the hash of that object.

Changed in version 2.6: __hash__ may now be set to None to explicitly flag
instances of a class as unhashable.

Called to implement truth value testing and the built-in operation bool();
should return False or True, or their integer equivalents 0 or
1. When this method is not defined, __len__() is called, if it is
defined, and the object is considered true if its result is nonzero.
If a class defines neither __len__() nor __nonzero__(), all its
instances are considered true.

Called to implement unicode() built-in; should return a Unicode object.
When this method is not defined, string conversion is attempted, and the result
of string conversion is converted to Unicode using the system default encoding.

Called when an attribute lookup has not found the attribute in the usual places
(i.e. it is not an instance attribute nor is it found in the class tree for
self). name is the attribute name. This method should return the
(computed) attribute value or raise an AttributeError exception.

Note that if the attribute is found through the normal mechanism,
__getattr__() is not called. (This is an intentional asymmetry between
__getattr__() and __setattr__().) This is done both for efficiency
reasons and because otherwise __getattr__() would have no way to access
other attributes of the instance. Note that at least for instance variables,
you can fake total control by not inserting any values in the instance attribute
dictionary (but instead inserting them in another object). See the
__getattribute__() method below for a way to actually get total control in
new-style classes.

Called when an attribute assignment is attempted. This is called instead of the
normal mechanism (i.e. store the value in the instance dictionary). name is
the attribute name, value is the value to be assigned to it.

If __setattr__() wants to assign to an instance attribute, it should not
simply execute self.name=value — this would cause a recursive call to
itself. Instead, it should insert the value in the dictionary of instance
attributes, e.g., self.__dict__[name]=value. For new-style classes,
rather than accessing the instance dictionary, it should call the base class
method with the same name, for example, object.__setattr__(self,name,value).

Called unconditionally to implement attribute accesses for instances of the
class. If the class also defines __getattr__(), the latter will not be
called unless __getattribute__() either calls it explicitly or raises an
AttributeError. This method should return the (computed) attribute value
or raise an AttributeError exception. In order to avoid infinite
recursion in this method, its implementation should always call the base class
method with the same name to access any attributes it needs, for example,
object.__getattribute__(self,name).

The following methods only apply when an instance of the class containing the
method (a so-called descriptor class) appears in an owner class (the
descriptor must be in either the owner’s class dictionary or in the class
dictionary for one of its parents). In the examples below, “the attribute”
refers to the attribute whose name is the key of the property in the owner
class’ __dict__.

Called to get the attribute of the owner class (class attribute access) or of an
instance of that class (instance attribute access). owner is always the owner
class, while instance is the instance that the attribute was accessed through,
or None when the attribute is accessed through the owner. This method
should return the (computed) attribute value or raise an AttributeError
exception.

In general, a descriptor is an object attribute with “binding behavior”, one
whose attribute access has been overridden by methods in the descriptor
protocol: __get__(), __set__(), and __delete__(). If any of
those methods are defined for an object, it is said to be a descriptor.

The default behavior for attribute access is to get, set, or delete the
attribute from an object’s dictionary. For instance, a.x has a lookup chain
starting with a.__dict__['x'], then type(a).__dict__['x'], and
continuing through the base classes of type(a) excluding metaclasses.

However, if the looked-up value is an object defining one of the descriptor
methods, then Python may override the default behavior and invoke the descriptor
method instead. Where this occurs in the precedence chain depends on which
descriptor methods were defined and how they were called. Note that descriptors
are only invoked for new style objects or classes (ones that subclass
object() or type()).

The starting point for descriptor invocation is a binding, a.x. How the
arguments are assembled depends on a:

Direct Call

The simplest and least common call is when user code directly invokes a
descriptor method: x.__get__(a).

Instance Binding

If binding to a new-style object instance, a.x is transformed into the call:
type(a).__dict__['x'].__get__(a,type(a)).

Class Binding

If binding to a new-style class, A.x is transformed into the call:
A.__dict__['x'].__get__(None,A).

Super Binding

If a is an instance of super, then the binding super(B,obj).m() searches obj.__class__.__mro__ for the base class A
immediately preceding B and then invokes the descriptor with the call:
A.__dict__['m'].__get__(obj,obj.__class__).

For instance bindings, the precedence of descriptor invocation depends on the
which descriptor methods are defined. A descriptor can define any combination
of __get__(), __set__() and __delete__(). If it does not
define __get__(), then accessing the attribute will return the descriptor
object itself unless there is a value in the object’s instance dictionary. If
the descriptor defines __set__() and/or __delete__(), it is a data
descriptor; if it defines neither, it is a non-data descriptor. Normally, data
descriptors define both __get__() and __set__(), while non-data
descriptors have just the __get__() method. Data descriptors with
__set__() and __get__() defined always override a redefinition in an
instance dictionary. In contrast, non-data descriptors can be overridden by
instances.

Python methods (including staticmethod() and classmethod()) are
implemented as non-data descriptors. Accordingly, instances can redefine and
override methods. This allows individual instances to acquire behaviors that
differ from other instances of the same class.

The property() function is implemented as a data descriptor. Accordingly,
instances cannot override the behavior of a property.

By default, instances of both old and new-style classes have a dictionary for
attribute storage. This wastes space for objects having very few instance
variables. The space consumption can become acute when creating large numbers
of instances.

The default can be overridden by defining __slots__ in a new-style class
definition. The __slots__ declaration takes a sequence of instance variables
and reserves just enough space in each instance to hold a value for each
variable. Space is saved because __dict__ is not created for each instance.

This class variable can be assigned a string, iterable, or sequence of strings
with variable names used by instances. If defined in a new-style class,
__slots__ reserves space for the declared variables and prevents the automatic
creation of __dict__ and __weakref__ for each instance.

New in version 2.2.

Notes on using __slots__

When inheriting from a class without __slots__, the __dict__ attribute of
that class will always be accessible, so a __slots__ definition in the
subclass is meaningless.

Without a __dict__ variable, instances cannot be assigned new variables not
listed in the __slots__ definition. Attempts to assign to an unlisted
variable name raises AttributeError. If dynamic assignment of new
variables is desired, then add '__dict__' to the sequence of strings in the
__slots__ declaration.

Changed in version 2.3: Previously, adding '__dict__' to the __slots__ declaration would not
enable the assignment of new attributes not specifically listed in the sequence
of instance variable names.

Without a __weakref__ variable for each instance, classes defining
__slots__ do not support weak references to its instances. If weak reference
support is needed, then add '__weakref__' to the sequence of strings in the
__slots__ declaration.

Changed in version 2.3: Previously, adding '__weakref__' to the __slots__ declaration would not
enable support for weak references.

__slots__ are implemented at the class level by creating descriptors
(Implementing Descriptors) for each variable name. As a result, class attributes
cannot be used to set default values for instance variables defined by
__slots__; otherwise, the class attribute would overwrite the descriptor
assignment.

The action of a __slots__ declaration is limited to the class where it is
defined. As a result, subclasses will have a __dict__ unless they also define
__slots__ (which must only contain names of any additional slots).

If a class defines a slot also defined in a base class, the instance variable
defined by the base class slot is inaccessible (except by retrieving its
descriptor directly from the base class). This renders the meaning of the
program undefined. In the future, a check may be added to prevent this.

Nonempty __slots__ does not work for classes derived from “variable-length”
built-in types such as long, str and tuple.

Any non-string iterable may be assigned to __slots__. Mappings may also be
used; however, in the future, special meaning may be assigned to the values
corresponding to each key.

__class__ assignment works only if both classes have the same __slots__.

Changed in version 2.6: Previously, __class__ assignment raised an error if either new or old class
had __slots__.

By default, new-style classes are constructed using type(). A class
definition is read into a separate namespace and the value of class name is
bound to the result of type(name,bases,dict).

When the class definition is read, if __metaclass__ is defined then the
callable assigned to it will be called instead of type(). This allows
classes or functions to be written which monitor or alter the class creation
process:

Modifying the class dictionary prior to the class being created.

Returning an instance of another class – essentially performing the role of a
factory function.

These steps will have to be performed in the metaclass’s __new__() method
– type.__new__() can then be called from this method to create a class
with different properties. This example adds a new element to the class
dictionary before creating the class:

classmetacls(type):def__new__(mcs,name,bases,dict):dict['foo']='metacls was here'returntype.__new__(mcs,name,bases,dict)

You can of course also override other class methods (or add new methods); for
example defining a custom __call__() method in the metaclass allows custom
behavior when the class is called, e.g. not always creating a new instance.

The potential uses for metaclasses are boundless. Some ideas that have been
explored including logging, interface checking, automatic delegation, automatic
property creation, proxies, frameworks, and automatic resource
locking/synchronization.

The following methods are used to override the default behavior of the
isinstance() and issubclass() built-in functions.

In particular, the metaclass abc.ABCMeta implements these methods in
order to allow the addition of Abstract Base Classes (ABCs) as “virtual base
classes” to any class or type (including built-in types), including other
ABCs.

Return true if subclass should be considered a (direct or indirect)
subclass of class. If defined, called to implement issubclass(subclass,class).

Note that these methods are looked up on the type (metaclass) of a class. They
cannot be defined as class methods in the actual class. This is consistent with
the lookup of special methods that are called on instances, only in this
case the instance is itself a class.

The following methods can be defined to implement container objects. Containers
usually are sequences (such as lists or tuples) or mappings (like dictionaries),
but can represent other containers as well. The first set of methods is used
either to emulate a sequence or to emulate a mapping; the difference is that for
a sequence, the allowable keys should be the integers k for which 0<=k<N where N is the length of the sequence, or slice objects, which define a
range of items. (For backwards compatibility, the method __getslice__()
(see below) can also be defined to handle simple, but not extended slices.) It
is also recommended that mappings provide the methods keys(),
values(), items(), has_key(), get(), clear(),
setdefault(), iterkeys(), itervalues(), iteritems(),
pop(), popitem(), copy(), and update() behaving similar
to those for Python’s standard dictionary objects. The UserDict module
provides a DictMixin class to help create those methods from a base set
of __getitem__(), __setitem__(), __delitem__(), and
keys(). Mutable sequences should provide methods append(),
count(), index(), extend(), insert(), pop(),
remove(), reverse() and sort(), like Python standard list
objects. Finally, sequence types should implement addition (meaning
concatenation) and multiplication (meaning repetition) by defining the methods
__add__(), __radd__(), __iadd__(), __mul__(),
__rmul__() and __imul__() described below; they should not define
__coerce__() or other numerical operators. It is recommended that both
mappings and sequences implement the __contains__() method to allow
efficient use of the in operator; for mappings, in should be equivalent
of has_key(); for sequences, it should search through the values. It is
further recommended that both mappings and sequences implement the
__iter__() method to allow efficient iteration through the container; for
mappings, __iter__() should be the same as iterkeys(); for
sequences, it should iterate through the values.

Called to implement the built-in function len(). Should return the length
of the object, an integer >= 0. Also, an object that doesn’t define a
__nonzero__() method and whose __len__() method returns zero is
considered to be false in a Boolean context.

Called to implement evaluation of self[key]. For sequence types, the
accepted keys should be integers and slice objects. Note that the special
interpretation of negative indexes (if the class wishes to emulate a sequence
type) is up to the __getitem__() method. If key is of an inappropriate
type, TypeError may be raised; if of a value outside the set of indexes
for the sequence (after any special interpretation of negative values),
IndexError should be raised. For mapping types, if key is missing (not
in the container), KeyError should be raised.

Note

for loops expect that an IndexError will be raised for illegal
indexes to allow proper detection of the end of the sequence.

Called to implement assignment to self[key]. Same note as for
__getitem__(). This should only be implemented for mappings if the
objects support changes to the values for keys, or if new keys can be added, or
for sequences if elements can be replaced. The same exceptions should be raised
for improper key values as for the __getitem__() method.

Called to implement deletion of self[key]. Same note as for
__getitem__(). This should only be implemented for mappings if the
objects support removal of keys, or for sequences if elements can be removed
from the sequence. The same exceptions should be raised for improper key
values as for the __getitem__() method.

This method is called when an iterator is required for a container. This method
should return a new iterator object that can iterate over all the objects in the
container. For mappings, it should iterate over the keys of the container, and
should also be made available as the method iterkeys().

Iterator objects also need to implement this method; they are required to return
themselves. For more information on iterator objects, see Iterator Types.

The membership test operators (in and notin) are normally
implemented as an iteration through a sequence. However, container objects can
supply the following special method with a more efficient implementation, which
also does not require the object be a sequence.

Called to implement membership test operators. Should return true if item
is in self, false otherwise. For mapping objects, this should consider the
keys of the mapping rather than the values or the key-item pairs.

The following optional methods can be defined to further emulate sequence
objects. Immutable sequences methods should at most only define
__getslice__(); mutable sequences might define all three methods.

Deprecated since version 2.0: Support slice objects as parameters to the __getitem__() method.
(However, built-in types in CPython currently still implement
__getslice__(). Therefore, you have to override it in derived
classes when implementing slicing.)

Called to implement evaluation of self[i:j]. The returned object should be
of the same type as self. Note that missing i or j in the slice
expression are replaced by zero or sys.maxint, respectively. If negative
indexes are used in the slice, the length of the sequence is added to that
index. If the instance does not implement the __len__() method, an
AttributeError is raised. No guarantee is made that indexes adjusted this
way are not still negative. Indexes which are greater than the length of the
sequence are not modified. If no __getslice__() is found, a slice object
is created instead, and passed to __getitem__() instead.

Called to implement deletion of self[i:j]. Same notes for i and j as for
__getslice__(). This method is deprecated. If no __delslice__() is
found, or for extended slicing of the form self[i:j:k], a slice object is
created, and passed to __delitem__(), instead of __delslice__()
being called.

Notice that these methods are only invoked when a single slice with a single
colon is used, and the slice method is available. For slice operations
involving extended slice notation, or in absence of the slice methods,
__getitem__(), __setitem__() or __delitem__() is called with a
slice object as argument.

The following example demonstrate how to make your program or module compatible
with earlier versions of Python (assuming that methods __getitem__(),
__setitem__() and __delitem__() support slice objects as
arguments):

classMyClass:...def__getitem__(self,index):...def__setitem__(self,index,value):...def__delitem__(self,index):...ifsys.version_info<(2,0):# They won't be defined if version is at least 2.0 finaldef__getslice__(self,i,j):returnself[max(0,i):max(0,j):]def__setslice__(self,i,j,seq):self[max(0,i):max(0,j):]=seqdef__delslice__(self,i,j):delself[max(0,i):max(0,j):]...

Note the calls to max(); these are necessary because of the handling of
negative indices before the __*slice__() methods are called. When
negative indexes are used, the __*item__() methods receive them as
provided, but the __*slice__() methods get a “cooked” form of the index
values. For each negative index value, the length of the sequence is added to
the index before calling the method (which may still result in a negative
index); this is the customary handling of negative indexes by the built-in
sequence types, and the __*item__() methods are expected to do this as
well. However, since they should already be doing that, negative indexes cannot
be passed in; they must be constrained to the bounds of the sequence before
being passed to the __*item__() methods. Calling max(0,i)
conveniently returns the proper value.

The following methods can be defined to emulate numeric objects. Methods
corresponding to operations that are not supported by the particular kind of
number implemented (e.g., bitwise operations for non-integral numbers) should be
left undefined.

These methods are called to implement the binary arithmetic operations (+,
-, *, //, %, divmod(), pow(), **, <<,
>>, &, ^, |). For instance, to evaluate the expression
x+y, where x is an instance of a class that has an __add__()
method, x.__add__(y) is called. The __divmod__() method should be the
equivalent to using __floordiv__() and __mod__(); it should not be
related to __truediv__() (described below). Note that __pow__()
should be defined to accept an optional third argument if the ternary version of
the built-in pow() function is to be supported.

If one of those methods does not support the operation with the supplied
arguments, it should return NotImplemented.

The division operator (/) is implemented by these methods. The
__truediv__() method is used when __future__.division is in effect,
otherwise __div__() is used. If only one of these two methods is defined,
the object will not support division in the alternate context; TypeError
will be raised instead.

These methods are called to implement the binary arithmetic operations (+,
-, *, /, %, divmod(), pow(), **, <<, >>,
&, ^, |) with reflected (swapped) operands. These functions are
only called if the left operand does not support the corresponding operation and
the operands are of different types. [2] For instance, to evaluate the
expression x-y, where y is an instance of a class that has an
__rsub__() method, y.__rsub__(x) is called if x.__sub__(y) returns
NotImplemented.

Note that ternary pow() will not try calling __rpow__() (the
coercion rules would become too complicated).

Note

If the right operand’s type is a subclass of the left operand’s type and that
subclass provides the reflected method for the operation, this method will be
called before the left operand’s non-reflected method. This behavior allows
subclasses to override their ancestors’ operations.

These methods are called to implement the augmented arithmetic assignments
(+=, -=, *=, /=, //=, %=, **=, <<=, >>=,
&=, ^=, |=). These methods should attempt to do the operation
in-place (modifying self) and return the result (which could be, but does
not have to be, self). If a specific method is not defined, the augmented
assignment falls back to the normal methods. For instance, to execute the
statement x+=y, where x is an instance of a class that has an
__iadd__() method, x.__iadd__(y) is called. If x is an instance
of a class that does not define a __iadd__() method, x.__add__(y)
and y.__radd__(x) are considered, as with the evaluation of x+y.

Called to implement “mixed-mode” numeric arithmetic. Should either return a
2-tuple containing self and other converted to a common numeric type, or
None if conversion is impossible. When the common type would be the type of
other, it is sufficient to return None, since the interpreter will also
ask the other object to attempt a coercion (but sometimes, if the implementation
of the other type cannot be changed, it is useful to do the conversion to the
other type here). A return value of NotImplemented is equivalent to
returning None.

This section used to document the rules for coercion. As the language has
evolved, the coercion rules have become hard to document precisely; documenting
what one version of one particular implementation does is undesirable. Instead,
here are some informal guidelines regarding coercion. In Python 3, coercion
will not be supported.

If the left operand of a % operator is a string or Unicode object, no coercion
takes place and the string formatting operation is invoked instead.

It is no longer recommended to define a coercion operation. Mixed-mode
operations on types that don’t define coercion pass the original arguments to
the operation.

New-style classes (those derived from object) never invoke the
__coerce__() method in response to a binary operator; the only time
__coerce__() is invoked is when the built-in function coerce() is
called.

For most intents and purposes, an operator that returns NotImplemented is
treated the same as one that is not implemented at all.

Below, __op__() and __rop__() are used to signify the generic method
names corresponding to an operator; __iop__() is used for the
corresponding in-place operator. For example, for the operator ‘+‘,
__add__() and __radd__() are used for the left and right variant of
the binary operator, and __iadd__() for the in-place variant.

For objects x and y, first x.__op__(y) is tried. If this is not
implemented or returns NotImplemented, y.__rop__(x) is tried. If this
is also not implemented or returns NotImplemented, a TypeError
exception is raised. But see the following exception:

Exception to the previous item: if the left operand is an instance of a built-in
type or a new-style class, and the right operand is an instance of a proper
subclass of that type or class and overrides the base’s __rop__() method,
the right operand’s __rop__() method is tried before the left operand’s
__op__() method.

This is done so that a subclass can completely override binary operators.
Otherwise, the left operand’s __op__() method would always accept the
right operand: when an instance of a given class is expected, an instance of a
subclass of that class is always acceptable.

When either operand type defines a coercion, this coercion is called before that
type’s __op__() or __rop__() method is called, but no sooner. If
the coercion returns an object of a different type for the operand whose
coercion is invoked, part of the process is redone using the new object.

When an in-place operator (like ‘+=‘) is used, if the left operand
implements __iop__(), it is invoked without any coercion. When the
operation falls back to __op__() and/or __rop__(), the normal
coercion rules apply.

In x+y, if x is a sequence that implements sequence concatenation,
sequence concatenation is invoked.

In x*y, if one operand is a sequence that implements sequence
repetition, and the other is an integer (int or long),
sequence repetition is invoked.

Rich comparisons (implemented by methods __eq__() and so on) never use
coercion. Three-way comparison (implemented by __cmp__()) does use
coercion under the same conditions as other binary operations use it.

In the current implementation, the built-in numeric types int,
long, float, and complex do not use coercion.
All these types implement a __coerce__() method, for use by the built-in
coerce() function.

Changed in version 2.7: The complex type no longer makes implicit calls to the __coerce__()
method for mixed-type binary arithmetic operations.

A context manager is an object that defines the runtime context to be
established when executing a with statement. The context manager
handles the entry into, and the exit from, the desired runtime context for the
execution of the block of code. Context managers are normally invoked using the
with statement (described in section The with statement), but can also be
used by directly invoking their methods.

Typical uses of context managers include saving and restoring various kinds of
global state, locking and unlocking resources, closing opened files, etc.

Exit the runtime context related to this object. The parameters describe the
exception that caused the context to be exited. If the context was exited
without an exception, all three arguments will be None.

If an exception is supplied, and the method wishes to suppress the exception
(i.e., prevent it from being propagated), it should return a true value.
Otherwise, the exception will be processed normally upon exit from this method.

Note that __exit__() methods should not reraise the passed-in exception;
this is the caller’s responsibility.

For old-style classes, special methods are always looked up in exactly the
same way as any other method or attribute. This is the case regardless of
whether the method is being looked up explicitly as in x.__getitem__(i)
or implicitly as in x[i].

This behaviour means that special methods may exhibit different behaviour
for different instances of a single old-style class if the appropriate
special attributes are set differently:

For new-style classes, implicit invocations of special methods are only guaranteed
to work correctly if defined on an object’s type, not in the object’s instance
dictionary. That behaviour is the reason why the following code raises an
exception (unlike the equivalent example with old-style classes):

The rationale behind this behaviour lies with a number of special methods such
as __hash__() and __repr__() that are implemented by all objects,
including type objects. If the implicit lookup of these methods used the
conventional lookup process, they would fail when invoked on the type object
itself:

Bypassing the __getattribute__() machinery in this fashion
provides significant scope for speed optimisations within the
interpreter, at the cost of some flexibility in the handling of
special methods (the special method must be set on the class
object itself in order to be consistently invoked by the interpreter).

It is possible in some cases to change an object’s type, under certain
controlled conditions. It generally isn’t a good idea though, since it can
lead to some very strange behaviour if it is handled incorrectly.